Salt model building at Atlantis with full-waveform inversion

Shen, Xuan; Ahmed, Imtiaz; Brenders, Andrew; Dellinger, Joe; Etgen, John; Michell, Scott

doi:10.1190/segam2017-17738630.1

Cited by 78 publications

(14 citation statements)

References 9 publications

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“…One successful example is the ultra-low-frequency marine seismic source, Wolfspar, designed by BP. It can produce the seismic signal as low as 1.6 Hz (Dellinger et al 2016), which leads to their huge success of applying FWI on the Atlantis oil field in the Gulf of Mexico (Shen et al 2017). BGP created an onshore controlled vibrator source, which can produce low frequencies as low as 1.5 Hz (Baeten et al 2013).…”

Section: Introductionmentioning

confidence: 99%

A review on reflection-waveform inversion

Yao

Wang

2020

Pet. Sci.

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Full-waveform inversion (FWI) utilizes optimization methods to recover an optimal Earth model to best fit the observed seismic record in a sense of a predefined norm. Since FWI combines mathematic inversion and full-wave equations, it has been recognized as one of the key methods for seismic data imaging and Earth model building in the fields of global/regional and exploration seismology. Unfortunately, conventional FWI fixes background velocity mainly relying on refraction and turning waves that are commonly rich in large offsets. By contrast, reflections in the short offsets mainly contribute to the reconstruction of the high-resolution interfaces. Restricted by acquisition geometries, refractions and turning waves in the record usually have limited penetration depth, which may not reach oil/gas reservoirs. Thus, reflections in the record are the only source that carries the information of these reservoirs. Consequently, it is meaningful to develop reflection-waveform inversion (RWI) that utilizes reflections to recover background velocity including the deep part of the model. This review paper includes: analyzing the weaknesses of FWI when inverting reflections; overviewing the principles of RWI, including separation of the tomography and migration components, the objective functions, constraints; summarizing the current status of the technique of RWI; outlooking the future of RWI.

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Section: Introductionmentioning

confidence: 99%

A review on reflection-waveform inversion

Yao

Wang

2020

Pet. Sci.

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“…Many successful salt inversions rely on data with low frequencies and long offsets. Shen et al (2017) implemented FWI using frequencies down to 1.6 Hz, full-azimuth, and long offset of about 20 km of ocean bottom nodes to invert for salt bodies in the Atlantis field.…”

Section: Accepted Articlementioning

confidence: 99%

Deep learning unflooding for robust subsalt waveform inversion

Alali

Kazei

Kalita

et al. 2022

Geophysical Prospecting

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“…Many successful examples have been reported using FWI to update shallow sediments, gas pockets, and mud volcanoes. However, the success of using FWI to update salt structures remained missing until the recent progress at the Atlantis field, Gulf of Mexico (GoM) using low-frequency, fullazimuth, and long-offset OBN data Shen et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…It is even harder if erroneous salt geometry is part of the velocity model. Shen et al (2017) showed a successful FWI application that corrected some misinterpretation of salt structures and resulted in improved subsalt images at the Atlantis field in the Gulf of Mexico. Their study stressed the importance of the low frequencies (usable down to 1.6 Hz), full azimuths, and long offsets of OBN data.…”

mentioning

confidence: 99%

Correcting for salt misinterpretation with full-waveform inversion

Zhang

Mei

Lin

et al. 2018

SEG Technical Program Expanded Abstracts 2018

114

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Using full-waveform inversion (FWI) to update velocity models that contain salt bodies with high velocity contrasts is challenging. It is even harder if erroneous salt geometry is part of the velocity model. Shen et al. (2017) showed a successful FWI application that corrected some misinterpretation of salt structures and resulted in improved subsalt images at the Atlantis field in the Gulf of Mexico. Their study stressed the importance of the low frequencies (usable down to 1.6 Hz), full azimuths, and long offsets of OBN data. Encouraged by the success at Atlantis, we revisited some aspects of FWI algorithms to minimize cycle-skipping and amplitude discrepancy issues that are common in the presence of salt and salt misinterpretation. Here we present the use of traveltime misfit measured in frequency-dependent time windows as the FWI cost function. It is devised to minimize the negative impact from the amplitude discrepancy and cycleskipping between the recorded data and modeled synthetic data. Furthermore, we use the crosscorrelation coefficient between the recorded data and synthetic data as a weight function in gradient computation to promote traveltime measurements of higher quality. We demonstrate the effect of our approach using a full-azimuth streamer data set in an area of complex shallow salt bodies in the Gulf of Mexico.

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Salt model building at Atlantis with full-waveform inversion

Cited by 78 publications

References 9 publications

A review on reflection-waveform inversion

A review on reflection-waveform inversion

Deep learning unflooding for robust subsalt waveform inversion

Correcting for salt misinterpretation with full-waveform inversion

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